from datetime import timedelta
import operator
from typing import Any, Callable, List, Optional, Sequence, Union

import numpy as np

from pandas._libs.tslibs import (
    NaT,
    NaTType,
    frequencies as libfrequencies,
    iNaT,
    period as libperiod,
)
from pandas._libs.tslibs.fields import isleapyear_arr
from pandas._libs.tslibs.period import (
    DIFFERENT_FREQ,
    IncompatibleFrequency,
    Period,
    get_period_field_arr,
    period_asfreq_arr,
)
from pandas._libs.tslibs.timedeltas import Timedelta, delta_to_nanoseconds
from pandas.util._decorators import cache_readonly

from pandas.core.dtypes.common import (
    _TD_DTYPE,
    ensure_object,
    is_datetime64_dtype,
    is_float_dtype,
    is_period_dtype,
    pandas_dtype,
)
from pandas.core.dtypes.dtypes import PeriodDtype
from pandas.core.dtypes.generic import ABCIndexClass, ABCPeriodIndex, ABCSeries
from pandas.core.dtypes.missing import isna, notna

import pandas.core.algorithms as algos
from pandas.core.arrays import datetimelike as dtl
import pandas.core.common as com

from pandas.tseries import frequencies
from pandas.tseries.offsets import DateOffset, Tick, _delta_to_tick


def _field_accessor(name: str, alias: int, docstring=None):
    def f(self):
        base, mult = libfrequencies.get_freq_code(self.freq)
        result = get_period_field_arr(alias, self.asi8, base)
        return result

    f.__name__ = name
    f.__doc__ = docstring
    return property(f)


class PeriodArray(dtl.DatetimeLikeArrayMixin, dtl.DatelikeOps):
    """
    Pandas ExtensionArray for storing Period data.

    Users should use :func:`period_array` to create new instances.

    Parameters
    ----------
    values : Union[PeriodArray, Series[period], ndarray[int], PeriodIndex]
        The data to store. These should be arrays that can be directly
        converted to ordinals without inference or copy (PeriodArray,
        ndarray[int64]), or a box around such an array (Series[period],
        PeriodIndex).
    freq : str or DateOffset
        The `freq` to use for the array. Mostly applicable when `values`
        is an ndarray of integers, when `freq` is required. When `values`
        is a PeriodArray (or box around), it's checked that ``values.freq``
        matches `freq`.
    dtype : PeriodDtype, optional
        A PeriodDtype instance from which to extract a `freq`. If both
        `freq` and `dtype` are specified, then the frequencies must match.
    copy : bool, default False
        Whether to copy the ordinals before storing.

    Attributes
    ----------
    None

    Methods
    -------
    None

    See Also
    --------
    period_array : Create a new PeriodArray.
    PeriodIndex : Immutable Index for period data.

    Notes
    -----
    There are two components to a PeriodArray

    - ordinals : integer ndarray
    - freq : pd.tseries.offsets.Offset

    The values are physically stored as a 1-D ndarray of integers. These are
    called "ordinals" and represent some kind of offset from a base.

    The `freq` indicates the span covered by each element of the array.
    All elements in the PeriodArray have the same `freq`.
    """

    # array priority higher than numpy scalars
    __array_priority__ = 1000
    _typ = "periodarray"  # ABCPeriodArray
    _scalar_type = Period
    _recognized_scalars = (Period,)
    _is_recognized_dtype = is_period_dtype

    # Names others delegate to us
    _other_ops: List[str] = []
    _bool_ops = ["is_leap_year"]
    _object_ops = ["start_time", "end_time", "freq"]
    _field_ops = [
        "year",
        "month",
        "day",
        "hour",
        "minute",
        "second",
        "weekofyear",
        "weekday",
        "week",
        "dayofweek",
        "dayofyear",
        "quarter",
        "qyear",
        "days_in_month",
        "daysinmonth",
    ]
    _datetimelike_ops = _field_ops + _object_ops + _bool_ops
    _datetimelike_methods = ["strftime", "to_timestamp", "asfreq"]

    # --------------------------------------------------------------------
    # Constructors

    def __init__(self, values, freq=None, dtype=None, copy=False):
        freq = validate_dtype_freq(dtype, freq)

        if freq is not None:
            freq = Period._maybe_convert_freq(freq)

        if isinstance(values, ABCSeries):
            values = values._values
            if not isinstance(values, type(self)):
                raise TypeError("Incorrect dtype")

        elif isinstance(values, ABCPeriodIndex):
            values = values._values

        if isinstance(values, type(self)):
            if freq is not None and freq != values.freq:
                raise raise_on_incompatible(values, freq)
            values, freq = values._data, values.freq

        values = np.array(values, dtype="int64", copy=copy)
        self._data = values
        if freq is None:
            raise ValueError("freq is not specified and cannot be inferred")
        self._dtype = PeriodDtype(freq)

    @classmethod
    def _simple_new(cls, values: np.ndarray, freq=None, **kwargs) -> "PeriodArray":
        # alias for PeriodArray.__init__
        assert isinstance(values, np.ndarray) and values.dtype == "i8"
        return cls(values, freq=freq, **kwargs)

    @classmethod
    def _from_sequence(
        cls,
        scalars: Sequence[Optional[Period]],
        dtype: Optional[PeriodDtype] = None,
        copy: bool = False,
    ) -> "PeriodArray":
        if dtype:
            freq = dtype.freq
        else:
            freq = None

        if isinstance(scalars, cls):
            validate_dtype_freq(scalars.dtype, freq)
            if copy:
                scalars = scalars.copy()
            assert isinstance(scalars, PeriodArray)  # for mypy
            return scalars

        periods = np.asarray(scalars, dtype=object)
        if copy:
            periods = periods.copy()

        freq = freq or libperiod.extract_freq(periods)
        ordinals = libperiod.extract_ordinals(periods, freq)
        return cls(ordinals, freq=freq)

    @classmethod
    def _from_sequence_of_strings(
        cls, strings, dtype=None, copy=False
    ) -> "PeriodArray":
        return cls._from_sequence(strings, dtype, copy)

    @classmethod
    def _from_datetime64(cls, data, freq, tz=None) -> "PeriodArray":
        """
        Construct a PeriodArray from a datetime64 array

        Parameters
        ----------
        data : ndarray[datetime64[ns], datetime64[ns, tz]]
        freq : str or Tick
        tz : tzinfo, optional

        Returns
        -------
        PeriodArray[freq]
        """
        data, freq = dt64arr_to_periodarr(data, freq, tz)
        return cls(data, freq=freq)

    @classmethod
    def _generate_range(cls, start, end, periods, freq, fields):
        periods = dtl.validate_periods(periods)

        if freq is not None:
            freq = Period._maybe_convert_freq(freq)

        field_count = len(fields)
        if start is not None or end is not None:
            if field_count > 0:
                raise ValueError(
                    "Can either instantiate from fields or endpoints, but not both"
                )
            subarr, freq = _get_ordinal_range(start, end, periods, freq)
        elif field_count > 0:
            subarr, freq = _range_from_fields(freq=freq, **fields)
        else:
            raise ValueError("Not enough parameters to construct Period range")

        return subarr, freq

    # -----------------------------------------------------------------
    # DatetimeLike Interface

    def _unbox_scalar(self, value: Union[Period, NaTType]) -> int:
        if value is NaT:
            return value.value
        elif isinstance(value, self._scalar_type):
            if not isna(value):
                self._check_compatible_with(value)
            return value.ordinal
        else:
            raise ValueError(f"'value' should be a Period. Got '{value}' instead.")

    def _scalar_from_string(self, value: str) -> Period:
        return Period(value, freq=self.freq)

    def _check_compatible_with(self, other, setitem: bool = False):
        if other is NaT:
            return
        if self.freqstr != other.freqstr:
            raise raise_on_incompatible(self, other)

    # --------------------------------------------------------------------
    # Data / Attributes

    @cache_readonly
    def dtype(self) -> PeriodDtype:
        return self._dtype

    # error: Read-only property cannot override read-write property  [misc]
    @property  # type: ignore
    def freq(self) -> DateOffset:
        """
        Return the frequency object for this PeriodArray.
        """
        return self.dtype.freq

    def __array__(self, dtype=None) -> np.ndarray:
        if dtype == "i8":
            return self.asi8
        elif dtype == bool:
            return ~self._isnan

        # This will raise TypeErorr for non-object dtypes
        return np.array(list(self), dtype=object)

    def __arrow_array__(self, type=None):
        """
        Convert myself into a pyarrow Array.
        """
        import pyarrow
        from pandas.core.arrays._arrow_utils import ArrowPeriodType

        if type is not None:
            if pyarrow.types.is_integer(type):
                return pyarrow.array(self._data, mask=self.isna(), type=type)
            elif isinstance(type, ArrowPeriodType):
                # ensure we have the same freq
                if self.freqstr != type.freq:
                    raise TypeError(
                        "Not supported to convert PeriodArray to array with different "
                        f"'freq' ({self.freqstr} vs {type.freq})"
                    )
            else:
                raise TypeError(
                    f"Not supported to convert PeriodArray to '{type}' type"
                )

        period_type = ArrowPeriodType(self.freqstr)
        storage_array = pyarrow.array(self._data, mask=self.isna(), type="int64")
        return pyarrow.ExtensionArray.from_storage(period_type, storage_array)

    # --------------------------------------------------------------------
    # Vectorized analogues of Period properties

    year = _field_accessor(
        "year",
        0,
        """
        The year of the period.
        """,
    )
    month = _field_accessor(
        "month",
        3,
        """
        The month as January=1, December=12.
        """,
    )
    day = _field_accessor(
        "day",
        4,
        """
        The days of the period.
        """,
    )
    hour = _field_accessor(
        "hour",
        5,
        """
        The hour of the period.
        """,
    )
    minute = _field_accessor(
        "minute",
        6,
        """
        The minute of the period.
        """,
    )
    second = _field_accessor(
        "second",
        7,
        """
        The second of the period.
        """,
    )
    weekofyear = _field_accessor(
        "week",
        8,
        """
        The week ordinal of the year.
        """,
    )
    week = weekofyear
    dayofweek = _field_accessor(
        "dayofweek",
        10,
        """
        The day of the week with Monday=0, Sunday=6.
        """,
    )
    weekday = dayofweek
    dayofyear = day_of_year = _field_accessor(
        "dayofyear",
        9,
        """
        The ordinal day of the year.
        """,
    )
    quarter = _field_accessor(
        "quarter",
        2,
        """
        The quarter of the date.
        """,
    )
    qyear = _field_accessor("qyear", 1)
    days_in_month = _field_accessor(
        "days_in_month",
        11,
        """
        The number of days in the month.
        """,
    )
    daysinmonth = days_in_month

    @property
    def is_leap_year(self) -> np.ndarray:
        """
        Logical indicating if the date belongs to a leap year.
        """
        return isleapyear_arr(np.asarray(self.year))

    @property
    def start_time(self):
        return self.to_timestamp(how="start")

    @property
    def end_time(self):
        return self.to_timestamp(how="end")

    def to_timestamp(self, freq=None, how="start"):
        """
        Cast to DatetimeArray/Index.

        Parameters
        ----------
        freq : str or DateOffset, optional
            Target frequency. The default is 'D' for week or longer,
            'S' otherwise.
        how : {'s', 'e', 'start', 'end'}
            Whether to use the start or end of the time period being converted.

        Returns
        -------
        DatetimeArray/Index
        """
        from pandas.core.arrays import DatetimeArray

        how = libperiod._validate_end_alias(how)

        end = how == "E"
        if end:
            if freq == "B":
                # roll forward to ensure we land on B date
                adjust = Timedelta(1, "D") - Timedelta(1, "ns")
                return self.to_timestamp(how="start") + adjust
            else:
                adjust = Timedelta(1, "ns")
                return (self + self.freq).to_timestamp(how="start") - adjust

        if freq is None:
            base, mult = libfrequencies.get_freq_code(self.freq)
            freq = libfrequencies.get_to_timestamp_base(base)
        else:
            freq = Period._maybe_convert_freq(freq)

        base, mult = libfrequencies.get_freq_code(freq)
        new_data = self.asfreq(freq, how=how)

        new_data = libperiod.periodarr_to_dt64arr(new_data.asi8, base)
        return DatetimeArray(new_data)._with_freq("infer")

    # --------------------------------------------------------------------

    def _time_shift(self, periods, freq=None):
        """
        Shift each value by `periods`.

        Note this is different from ExtensionArray.shift, which
        shifts the *position* of each element, padding the end with
        missing values.

        Parameters
        ----------
        periods : int
            Number of periods to shift by.
        freq : pandas.DateOffset, pandas.Timedelta, or str
            Frequency increment to shift by.
        """
        if freq is not None:
            raise TypeError(
                "`freq` argument is not supported for "
                f"{type(self).__name__}._time_shift"
            )
        values = self.asi8 + periods * self.freq.n
        if self._hasnans:
            values[self._isnan] = iNaT
        return type(self)(values, freq=self.freq)

    @property
    def _box_func(self):
        return lambda x: Period._from_ordinal(ordinal=x, freq=self.freq)

    def asfreq(self, freq=None, how="E") -> "PeriodArray":
        """
        Convert the Period Array/Index to the specified frequency `freq`.

        Parameters
        ----------
        freq : str
            A frequency.
        how : str {'E', 'S'}
            Whether the elements should be aligned to the end
            or start within pa period.

            * 'E', 'END', or 'FINISH' for end,
            * 'S', 'START', or 'BEGIN' for start.

            January 31st ('END') vs. January 1st ('START') for example.

        Returns
        -------
        Period Array/Index
            Constructed with the new frequency.

        Examples
        --------
        >>> pidx = pd.period_range('2010-01-01', '2015-01-01', freq='A')
        >>> pidx
        PeriodIndex(['2010', '2011', '2012', '2013', '2014', '2015'],
        dtype='period[A-DEC]', freq='A-DEC')

        >>> pidx.asfreq('M')
        PeriodIndex(['2010-12', '2011-12', '2012-12', '2013-12', '2014-12',
        '2015-12'], dtype='period[M]', freq='M')

        >>> pidx.asfreq('M', how='S')
        PeriodIndex(['2010-01', '2011-01', '2012-01', '2013-01', '2014-01',
        '2015-01'], dtype='period[M]', freq='M')
        """
        how = libperiod._validate_end_alias(how)

        freq = Period._maybe_convert_freq(freq)

        base1, mult1 = libfrequencies.get_freq_code(self.freq)
        base2, mult2 = libfrequencies.get_freq_code(freq)

        asi8 = self.asi8
        # mult1 can't be negative or 0
        end = how == "E"
        if end:
            ordinal = asi8 + mult1 - 1
        else:
            ordinal = asi8

        new_data = period_asfreq_arr(ordinal, base1, base2, end)

        if self._hasnans:
            new_data[self._isnan] = iNaT

        return type(self)(new_data, freq=freq)

    # ------------------------------------------------------------------
    # Rendering Methods

    def _formatter(self, boxed: bool = False):
        if boxed:
            return str
        return "'{}'".format

    def _format_native_types(self, na_rep="NaT", date_format=None, **kwargs):
        """
        actually format my specific types
        """
        values = self.astype(object)

        if date_format:
            formatter = lambda dt: dt.strftime(date_format)
        else:
            formatter = lambda dt: str(dt)

        if self._hasnans:
            mask = self._isnan
            values[mask] = na_rep
            imask = ~mask
            values[imask] = np.array([formatter(dt) for dt in values[imask]])
        else:
            values = np.array([formatter(dt) for dt in values])
        return values

    # ------------------------------------------------------------------

    def astype(self, dtype, copy: bool = True):
        # We handle Period[T] -> Period[U]
        # Our parent handles everything else.
        dtype = pandas_dtype(dtype)

        if is_period_dtype(dtype):
            return self.asfreq(dtype.freq)
        return super().astype(dtype, copy=copy)

    # ------------------------------------------------------------------
    # Arithmetic Methods

    def _sub_datelike(self, other):
        assert other is not NaT
        return NotImplemented

    def _sub_period(self, other):
        # If the operation is well-defined, we return an object-Index
        # of DateOffsets.  Null entries are filled with pd.NaT
        self._check_compatible_with(other)
        asi8 = self.asi8
        new_data = asi8 - other.ordinal
        new_data = np.array([self.freq * x for x in new_data])

        if self._hasnans:
            new_data[self._isnan] = NaT

        return new_data

    def _addsub_int_array(
        self, other: np.ndarray, op: Callable[[Any, Any], Any],
    ) -> "PeriodArray":
        """
        Add or subtract array of integers; equivalent to applying
        `_time_shift` pointwise.

        Parameters
        ----------
        other : np.ndarray[integer-dtype]
        op : {operator.add, operator.sub}

        Returns
        -------
        result : PeriodArray
        """
        assert op in [operator.add, operator.sub]
        if op is operator.sub:
            other = -other
        res_values = algos.checked_add_with_arr(self.asi8, other, arr_mask=self._isnan)
        res_values = res_values.view("i8")
        res_values[self._isnan] = iNaT
        return type(self)(res_values, freq=self.freq)

    def _add_offset(self, other):
        assert not isinstance(other, Tick)
        base = libfrequencies.get_base_alias(other.rule_code)
        if base != self.freq.rule_code:
            raise raise_on_incompatible(self, other)

        # Note: when calling parent class's _add_timedeltalike_scalar,
        #  it will call delta_to_nanoseconds(delta).  Because delta here
        #  is an integer, delta_to_nanoseconds will return it unchanged.
        result = super()._add_timedeltalike_scalar(other.n)
        return type(self)(result, freq=self.freq)

    def _add_timedeltalike_scalar(self, other):
        """
        Parameters
        ----------
        other : timedelta, Tick, np.timedelta64

        Returns
        -------
        PeriodArray
        """
        if not isinstance(self.freq, Tick):
            # We cannot add timedelta-like to non-tick PeriodArray
            raise raise_on_incompatible(self, other)

        if notna(other):
            # special handling for np.timedelta64("NaT"), avoid calling
            #  _check_timedeltalike_freq_compat as that would raise TypeError
            other = self._check_timedeltalike_freq_compat(other)

        # Note: when calling parent class's _add_timedeltalike_scalar,
        #  it will call delta_to_nanoseconds(delta).  Because delta here
        #  is an integer, delta_to_nanoseconds will return it unchanged.
        return super()._add_timedeltalike_scalar(other)

    def _add_timedelta_arraylike(self, other):
        """
        Parameters
        ----------
        other : TimedeltaArray or ndarray[timedelta64]

        Returns
        -------
        result : ndarray[int64]
        """
        if not isinstance(self.freq, Tick):
            # We cannot add timedelta-like to non-tick PeriodArray
            raise raise_on_incompatible(self, other)

        if not np.all(isna(other)):
            delta = self._check_timedeltalike_freq_compat(other)
        else:
            # all-NaT TimedeltaIndex is equivalent to a single scalar td64 NaT
            return self + np.timedelta64("NaT")

        ordinals = self._addsub_int_array(delta, operator.add).asi8
        return type(self)(ordinals, dtype=self.dtype)

    def _check_timedeltalike_freq_compat(self, other):
        """
        Arithmetic operations with timedelta-like scalars or array `other`
        are only valid if `other` is an integer multiple of `self.freq`.
        If the operation is valid, find that integer multiple.  Otherwise,
        raise because the operation is invalid.

        Parameters
        ----------
        other : timedelta, np.timedelta64, Tick,
                ndarray[timedelta64], TimedeltaArray, TimedeltaIndex

        Returns
        -------
        multiple : int or ndarray[int64]

        Raises
        ------
        IncompatibleFrequency
        """
        assert isinstance(self.freq, Tick)  # checked by calling function
        own_offset = frequencies.to_offset(self.freq.rule_code)
        base_nanos = delta_to_nanoseconds(own_offset)

        if isinstance(other, (timedelta, np.timedelta64, Tick)):
            nanos = delta_to_nanoseconds(other)

        elif isinstance(other, np.ndarray):
            # numpy timedelta64 array; all entries must be compatible
            assert other.dtype.kind == "m"
            if other.dtype != _TD_DTYPE:
                # i.e. non-nano unit
                # TODO: disallow unit-less timedelta64
                other = other.astype(_TD_DTYPE)
            nanos = other.view("i8")
        else:
            # TimedeltaArray/Index
            nanos = other.asi8

        if np.all(nanos % base_nanos == 0):
            # nanos being added is an integer multiple of the
            #  base-frequency to self.freq
            delta = nanos // base_nanos
            # delta is the integer (or integer-array) number of periods
            # by which will be added to self.
            return delta

        raise raise_on_incompatible(self, other)


def raise_on_incompatible(left, right):
    """
    Helper function to render a consistent error message when raising
    IncompatibleFrequency.

    Parameters
    ----------
    left : PeriodArray
    right : None, DateOffset, Period, ndarray, or timedelta-like

    Returns
    -------
    IncompatibleFrequency
        Exception to be raised by the caller.
    """
    # GH#24283 error message format depends on whether right is scalar
    if isinstance(right, np.ndarray) or right is None:
        other_freq = None
    elif isinstance(right, (ABCPeriodIndex, PeriodArray, Period, DateOffset)):
        other_freq = right.freqstr
    else:
        other_freq = _delta_to_tick(Timedelta(right)).freqstr

    msg = DIFFERENT_FREQ.format(
        cls=type(left).__name__, own_freq=left.freqstr, other_freq=other_freq
    )
    return IncompatibleFrequency(msg)


# -------------------------------------------------------------------
# Constructor Helpers


def period_array(
    data: Sequence[Optional[Period]],
    freq: Optional[Union[str, Tick]] = None,
    copy: bool = False,
) -> PeriodArray:
    """
    Construct a new PeriodArray from a sequence of Period scalars.

    Parameters
    ----------
    data : Sequence of Period objects
        A sequence of Period objects. These are required to all have
        the same ``freq.`` Missing values can be indicated by ``None``
        or ``pandas.NaT``.
    freq : str, Tick, or Offset
        The frequency of every element of the array. This can be specified
        to avoid inferring the `freq` from `data`.
    copy : bool, default False
        Whether to ensure a copy of the data is made.

    Returns
    -------
    PeriodArray

    See Also
    --------
    PeriodArray
    pandas.PeriodIndex

    Examples
    --------
    >>> period_array([pd.Period('2017', freq='A'),
    ...               pd.Period('2018', freq='A')])
    <PeriodArray>
    ['2017', '2018']
    Length: 2, dtype: period[A-DEC]

    >>> period_array([pd.Period('2017', freq='A'),
    ...               pd.Period('2018', freq='A'),
    ...               pd.NaT])
    <PeriodArray>
    ['2017', '2018', 'NaT']
    Length: 3, dtype: period[A-DEC]

    Integers that look like years are handled

    >>> period_array([2000, 2001, 2002], freq='D')
    ['2000-01-01', '2001-01-01', '2002-01-01']
    Length: 3, dtype: period[D]

    Datetime-like strings may also be passed

    >>> period_array(['2000-Q1', '2000-Q2', '2000-Q3', '2000-Q4'], freq='Q')
    <PeriodArray>
    ['2000Q1', '2000Q2', '2000Q3', '2000Q4']
    Length: 4, dtype: period[Q-DEC]
    """
    if is_datetime64_dtype(data):
        return PeriodArray._from_datetime64(data, freq)
    if isinstance(data, (ABCPeriodIndex, ABCSeries, PeriodArray)):
        return PeriodArray(data, freq)

    # other iterable of some kind
    if not isinstance(data, (np.ndarray, list, tuple)):
        data = list(data)

    data = np.asarray(data)

    dtype: Optional[PeriodDtype]
    if freq:
        dtype = PeriodDtype(freq)
    else:
        dtype = None

    if is_float_dtype(data) and len(data) > 0:
        raise TypeError("PeriodIndex does not allow floating point in construction")

    data = ensure_object(data)

    return PeriodArray._from_sequence(data, dtype=dtype)


def validate_dtype_freq(dtype, freq):
    """
    If both a dtype and a freq are available, ensure they match.  If only
    dtype is available, extract the implied freq.

    Parameters
    ----------
    dtype : dtype
    freq : DateOffset or None

    Returns
    -------
    freq : DateOffset

    Raises
    ------
    ValueError : non-period dtype
    IncompatibleFrequency : mismatch between dtype and freq
    """
    if freq is not None:
        freq = frequencies.to_offset(freq)

    if dtype is not None:
        dtype = pandas_dtype(dtype)
        if not is_period_dtype(dtype):
            raise ValueError("dtype must be PeriodDtype")
        if freq is None:
            freq = dtype.freq
        elif freq != dtype.freq:
            raise IncompatibleFrequency("specified freq and dtype are different")
    return freq


def dt64arr_to_periodarr(data, freq, tz=None):
    """
    Convert an datetime-like array to values Period ordinals.

    Parameters
    ----------
    data : Union[Series[datetime64[ns]], DatetimeIndex, ndarray[datetime64ns]]
    freq : Optional[Union[str, Tick]]
        Must match the `freq` on the `data` if `data` is a DatetimeIndex
        or Series.
    tz : Optional[tzinfo]

    Returns
    -------
    ordinals : ndarray[int]
    freq : Tick
        The frequency extracted from the Series or DatetimeIndex if that's
        used.

    """
    if data.dtype != np.dtype("M8[ns]"):
        raise ValueError(f"Wrong dtype: {data.dtype}")

    if freq is None:
        if isinstance(data, ABCIndexClass):
            data, freq = data._values, data.freq
        elif isinstance(data, ABCSeries):
            data, freq = data._values, data.dt.freq

    freq = Period._maybe_convert_freq(freq)

    if isinstance(data, (ABCIndexClass, ABCSeries)):
        data = data._values

    base, mult = libfrequencies.get_freq_code(freq)
    return libperiod.dt64arr_to_periodarr(data.view("i8"), base, tz), freq


def _get_ordinal_range(start, end, periods, freq, mult=1):
    if com.count_not_none(start, end, periods) != 2:
        raise ValueError(
            "Of the three parameters: start, end, and periods, "
            "exactly two must be specified"
        )

    if freq is not None:
        _, mult = libfrequencies.get_freq_code(freq)

    if start is not None:
        start = Period(start, freq)
    if end is not None:
        end = Period(end, freq)

    is_start_per = isinstance(start, Period)
    is_end_per = isinstance(end, Period)

    if is_start_per and is_end_per and start.freq != end.freq:
        raise ValueError("start and end must have same freq")
    if start is NaT or end is NaT:
        raise ValueError("start and end must not be NaT")

    if freq is None:
        if is_start_per:
            freq = start.freq
        elif is_end_per:
            freq = end.freq
        else:  # pragma: no cover
            raise ValueError("Could not infer freq from start/end")

    if periods is not None:
        periods = periods * mult
        if start is None:
            data = np.arange(
                end.ordinal - periods + mult, end.ordinal + 1, mult, dtype=np.int64
            )
        else:
            data = np.arange(
                start.ordinal, start.ordinal + periods, mult, dtype=np.int64
            )
    else:
        data = np.arange(start.ordinal, end.ordinal + 1, mult, dtype=np.int64)

    return data, freq


def _range_from_fields(
    year=None,
    month=None,
    quarter=None,
    day=None,
    hour=None,
    minute=None,
    second=None,
    freq=None,
):
    if hour is None:
        hour = 0
    if minute is None:
        minute = 0
    if second is None:
        second = 0
    if day is None:
        day = 1

    ordinals = []

    if quarter is not None:
        if freq is None:
            freq = "Q"
            base = libfrequencies.FreqGroup.FR_QTR
        else:
            base, mult = libfrequencies.get_freq_code(freq)
            if base != libfrequencies.FreqGroup.FR_QTR:
                raise AssertionError("base must equal FR_QTR")

        year, quarter = _make_field_arrays(year, quarter)
        for y, q in zip(year, quarter):
            y, m = libperiod.quarter_to_myear(y, q, freq)
            val = libperiod.period_ordinal(y, m, 1, 1, 1, 1, 0, 0, base)
            ordinals.append(val)
    else:
        base, mult = libfrequencies.get_freq_code(freq)
        arrays = _make_field_arrays(year, month, day, hour, minute, second)
        for y, mth, d, h, mn, s in zip(*arrays):
            ordinals.append(libperiod.period_ordinal(y, mth, d, h, mn, s, 0, 0, base))

    return np.array(ordinals, dtype=np.int64), freq


def _make_field_arrays(*fields):
    length = None
    for x in fields:
        if isinstance(x, (list, np.ndarray, ABCSeries)):
            if length is not None and len(x) != length:
                raise ValueError("Mismatched Period array lengths")
            elif length is None:
                length = len(x)

    arrays = [
        np.asarray(x)
        if isinstance(x, (np.ndarray, list, ABCSeries))
        else np.repeat(x, length)
        for x in fields
    ]

    return arrays
